Search Results (1,393)

Compared with mass spectrometry or high-performance liquid chromatography (HPLC), surface-enhanced Raman scattering (SERS) is a promising alternative technique for inspection of preservatives in food safety. However, conventional SERS substrates based on metallic nanoparticles commonly suffer from complicated fabrication processes, long processing times, and high costs. Therefore, we propose a high-density porous anodic aluminum oxide (AAO) substrate prepared by one-step anodization process combined with pore widening to increase number of SERS hotspots on template. Through a rapid one-step anodization process conducted at 25 °C, the processing time and efficiency are greatly improved compared to conventional low temperature of 0–10 °C and two-step anodization method. By lowering the anodization voltage to 20 V, a high-density porous substrate is achieved, effectively enhancing the SERS signal intensity. Furthermore, we demonstrated that SERS signal intensities are affected by multiple correlated structural factors and significantly improved by lower anodization voltage with pore widening. The analytical enhancement factor is calculated as 1.18 × 10⁵ to 1.44 × 10⁷ on an AAO substrate prepared at 20 V with pore-widening process for 1000 and 0.1 ppm p-hydroxybenzoic acid, respectively. For the preservative detection of p-hydroxybenzoic acid, a detection limit of 100 ppb is achieved by a high-density AAO substrate prepared at 20 V, which is far below the regulatory limit of 600 ppm. Full article

Surface-enhanced Raman spectroscopy (SERS) has emerged as a transformative tool in biomedical diagnostics, offering a highly sensitive and non-invasive method for detecting molecular biomarkers at exceptionally low concentrations. This approach takes advantage of the plasmonic characteristics of customized metallic nanostructures that produce intense localized electromagnetic fields via localized surface plasmon resonance and facilitate electron transfer reactions that notoriously enhance the intrinsically weak Raman scattering signals of molecular entities which reside on or next to their surfaces. SERS-based assays have shown remarkable potential in detecting cancer biomarkers, circulating tumor DNA (ctDNA), and proteins at early stages, enabling timely and targeted intervention. Additionally, the combination of SERS with AI-driven data analysis has facilitated real-time diagnostics, enhancing the precision and efficiency of point-of-care testing. Despite its promising capabilities, challenges such as substrate fouling, signal degradation, and the need for better biocompatibility remain. Nevertheless, ongoing research in substrate development, coupled with advances in AI, positions SERS as a leading technology for future diagnostic tools. This paper explores the current state of SERS in biomedical applications, highlighting its potential to revolutionize diagnostics and personalized medicine while addressing the existing limitations and future research directions. Full article

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. This review provides a comprehensive and critical synthesis of AI-assisted SERS platforms for cardiovascular diagnostics, integrating advances in plasmonic materials, biomolecular recognition, and intelligent spectral analysis within a unified framework. It further examines key translational barriers, including data variability, model interpretability, and scalability, and outlines future directions for developing standardized, edge-deployable, and clinically validated SERS-AI systems. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Human saliva is a heterogeneous bodily fluid with a complex composition, which contains antibodies, proteins, and viruses, making it applicable in clinical diagnosis. There are several advantages of the analysis of saliva samples over other biofluids, including a non-invasive and simple collection procedure for extraoral detection. Biomarker or pathogen detection in saliva can be performed with various methods: mass spectrometry, PCR, ELISA, electrochemical, and optical methods such as fluorescence, SPR, and SERS. The early detection of cancer and other disease biomarkers, as well as infectious agents, can be crucial for effective treatment and minimization of mortality from those diseases. The following paper reviews extraoral detection techniques to identify the most sensitive methods for diagnosing early and asymptomatic patients. The LODs collected and tabulated from 149 analytical papers, alongside the sensitivity, specificity, and sometimes the area under the curve (AUC) tabulated from 118 clinical studies, have all become parameters for the comparative quantitative analysis. Based on the limited but substantial number of analytical studies on the detection of cortisol in saliva (29), the electrochemical platforms demonstrated the highest sensitivity, with a geometric mean LOD of 11 pM. Within these methods, voltametric ones showed the best performance with 6 pM geometric mean LOD. Electrochemical techniques are then followed by immunoassay- and mass spectrometry-based platforms, with corresponding geometric average LOD values of 39.1 and 171 pM, respectively. However, clinical outcomes are at least as meaningful as LOD values. In terms of clinical analysis, ELISA and direct-SERS outperformed other methods, achieving balanced accuracy of approximately 87% and AUC values of 0.96 for direct SERS and 0.86 for ELISA. MS and PCR followed closely, with balanced accuracies around 84%. While the direct SERS is not yet widespread in clinical applications, its potential can be forged if the standardization issue is addressed. Full article

Modern cell imaging is increasingly evolving toward high-content, label-free, and spectrally rich analytical approaches capable of resolving biochemical heterogeneity at cellular and subcellular levels. Raman microspectroscopy (µRS) and surface-enhanced Raman scattering (SERS) provide molecularly specific vibrational fingerprints with minimal photobleaching and high multiplexing capability, making them attractive tools for biomedical imaging and cellular analysis. However, broader implementation remains limited by weak intrinsic signals, insufficient targeting specificity, and limited control over nanoscale sensing environments in complex biological systems. Metal–organic framework (MOF) nanoparticles have recently emerged as promising platforms to address these challenges by offering porous, chemically tunable, and structurally well-defined scaffolds for Raman- and SERS-active nanostructures. Their high stability and favourable biocompatibility further support integration into biological applications. This review summarizes recent advances in MOF-assisted µRS and SERS across chemical sensing, bioanalytical detection, and biomedical diagnostics, with particular emphasis on cellular and subcellular imaging. Unlike previous reviews focused primarily on sensing performance, this work highlights the emerging role of MOF-SERS systems in high-content cellular imaging and evaluates their translation toward biologically relevant environments. Key design strategies and current challenges are critically discussed. Full article

Alzheimer’s disease (AD) is characterized by Tau hyperphosphorylation, β-amyloid (Aβ) accumulation, and progressive neuronal loss. Gastrodia elata (G. elata), a traditional Chinese medicine with well-established neuroprotective properties, was investigated. Two G. elata-derived miRNAs, Gas-miR04-3p and Gas-miR19-5p, were identified as regulators of JNK3. By means of Western blot, RT-qPCR, and assessments of antioxidant indices, it was demonstrated that Gas-miR04-3p and Gas-miR19-5p can suppress JNK3 expression, reduce Tau phosphorylation at Ser202 and Ser396, enhance antioxidant capacity, and attenuate apoptosis in AD-related cellular and molecular pathology models. These miRNAs were also detectable in murine brain tissues following oral administration of total RNA extracted from G. elata. Their administration was associated with decreased JNK3 activation, alleviated Tau hyperphosphorylation, and improved expression of apoptosis-related proteins in AD mouse models. These results suggest that G. elata miRNAs may exert neuroprotective effects through regulation of JNK3 signaling, thereby attenuating Tau-related pathological changes and neuronal injury in AD-related models. Full article

Fifty-five batik textiles produced along coastal Java in the late 19th to early 20th century were analysed to study the red dyes and the cotton fabrics. Surface-Enhanced Raman Spectroscopy (SERS) classified the dyes into six groups and identified 70% of the samples as Morinda. High-Performance Liquid Chromatography coupled with a diode array detector and tandem Mass Spectrometry (HPLC-DAD-MS/MS) confirmed the SERS results and identified synthetic dyes in the remaining samples, which were used either alone or in mixtures with Morinda or indigo. Synthetic alizarin (C.I. 58000, Mordant Red 11) was the most frequently detected synthetic dye. Auramine O (C.I. 41000, Basic Yellow 2), fuchsin (C.I. 42510, Basic Violet 14), and rhodamine B (C.I. 45170, Basic Violet 10) were occasionally detected. The results also highlighted two possible types of Morinda and two variations of synthetic alizarin. The shades obtained from mixtures of natural and synthetic dyes were visually indistinguishable from those obtained with pure natural or synthetic dye, as confirmed by colourimetry. The variety of dyes and cotton fabrics shared across batik producers makes it challenging to attribute unsigned batiks to specific workshops. Nevertheless, this study demonstrated that synthetic dye uptake during this period was limited and experimental, with natural Morinda remaining the preferred choice despite the availability of European synthetic alternatives. Full article

Crohn’s disease (CD) is a chronic inflammatory bowel disease requiring accurate and timely diagnosis. Current diagnostic tools may be invasive, costly, or insufficiently specific. Surface-enhanced Raman spectroscopy may enable rapid, minimally invasive detection of disease-associated biochemical alterations in serum. This cross-sectional pilot study included age- and sex-matched patients with Crohn’s disease and healthy controls. Serum samples were analyzed using surface-enhanced Raman spectroscopy. Spectral data were preprocessed and analyzed using principal component analysis-linear discriminant analysis and partial least squares-discriminant analysis. Classification performance was evaluated using leave-one-out cross-validation. Variable importance in projection scores was used to identify discriminatory vibrational bands. Fifty-four participants fulfilled the clinical inclusion criteria, while 51 participant-level spectra were retained for final classification analysis. PCA-LDA differentiated CD from healthy controls with a sensitivity of 85.19%, specificity of 91.67%, accuracy of 88.24%, and AUC of 0.881. PLS-DA showed slightly higher performance, with a sensitivity of 88.89%, specificity of 95.83%, accuracy of 92.16%, and AUC of 0.937. Relevant discriminatory bands were observed at 498, 639, 728, 813, 1136, 1205, 1443, 1579, and 1657 cm⁻¹, suggesting alterations in purine metabolism, protein structure, lipid composition, and nucleic acid-associated signals. Serum-based SERS combined with multivariate analysis showed promising ability to distinguish patients with CD from healthy controls in this pilot cohort. Larger multicenter studies are required to validate these findings and assess clinical applicability. Full article

Dental composite dust generated during finishing procedures or mastication may adversely affect gingival epithelia. However, the mechanistic distinction between particulate and chemical (eluate) exposures and their respective signaling consequences remains insufficiently defined. Dust particles and corresponding eluates from three restorative composites, Admira Fusion, Ceram.x Spectra ST, and Filtek Supreme XTE, were evaluated under standardized high-dose in vitro exposure conditions. Human gingival keratinocytes were assessed for proliferation, adhesion, differentiation, fibronectin (FN1) remodeling, and IL-8 secretion, alongside analysis of ERK, p38, and NF-κB signaling and phosphorylation of the stress-responsive regulator SIRT1 at Ser682 (SIRT1-S682). Particulate exposure elicited more pronounced impairment of cellular adhesion, proliferation, and differentiation than eluates. Dusts derived from Ceram.x Spectra ST and Filtek Supreme XTE suppressed ERK activity, reduced FN1 abundance, and decreased nuclear SIRT1-S682, consistent with a generalized stress response. In contrast, Admira Fusion dust preserved FN1, activated ERK signaling, reduced SIRT1-S682, and induced robust IL-8 secretion. Across all materials, particulate exposure reduced nuclear SIRT1-S682 without affecting total SIRT1 levels, indicating a shared permissive stress modification. Notably, only Admira Fusion coupled this permissive state with p38 activation and sustained NF-κB p65 Ser536 phosphorylation, resulting in transcriptionally active NF-κB and elevated IL-8 production, whereas Ceram.x Spectra ST and Filtek Supreme XTE failed to activate this ERK–FN1–p38–NF-κB axis, yielding either transcriptionally inactive NF-κB or no detectable enrichment. These findings support a material-associated in vitro response pattern in which a shared SIRT1-S682 reduction is accompanied by distinct ERK/FN1, p38, NF-κB, and IL-8 readouts. SIRT1-S682 reduction alone did not define the inflammatory phenotype, because it occurred across particulate exposures, whereas IL-8 secretion was observed only under conditions that also showed p38 activation and comparatively maintained NF-κB p65 Ser536 phosphorylation. This signature arises from the convergence of a permissive SIRT1-S682 background with ERK- and p38-dependent MAPK signaling to enable NF-κB-mediated IL-8 expression, highlighting that both composite composition and particulate properties critically determine inflammatory potential and underscoring the importance of incorporating particulate fractions into cytocompatibility testing strategies. Full article

Background/Objectives: Very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) is an autosomal recessive disorder of mitochondrial fatty acid β-oxidation caused by pathogenic variants in ACADVL. The clinical spectrum is highly heterogeneous, ranging from lethal neonatal cardiomyopathy to late-onset myopathy. This study aims to characterize the rare c.215C>T (p.Ser72Phe) variant, identified in compound heterozygosity with the common pathogenic allele c.848T>C (p.Val283Ala) in a male neonate detected by newborn screening (NBS). Methods: Genetic analysis was performed using Sanger sequencing on the proband and his family members. The pathogenicity of the p.Ser72Phe variant was evaluated through multiple bioinformatic predictors and interpreted according to ACMG/AMP guidelines. To understand the functional impact on the protein, structural modeling was conducted using FoldX 4.0 for energy calculations and UCSF ChimeraX for the visualization of conformational changes and cofactor-binding site perturbations in the VLCAD homodimer. Results: At the end of the first postnatal week, liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis of dried blood spots of the proband revealed a markedly abnormal acylcarnitine profile, with C14:1 levels (1.837 μmol/L) approximately five times above the reference range. Clinical reports documented hypoketotic hypoglycemia, consistent with VLCADD. Segregation analysis demonstrated transmission of both variants within the family, with additional heterozygous and homozygous carriers identified. Bioinformatic predictions uniformly classified p.Ser72Phe as deleterious. This variant has an extremely low allele frequency and affects a highly conserved residue in the FAD-binding domain. Structural modeling with FoldX yielded a mean ΔΔG of +22.63 ± 5.48 kcal/mol, indicating a significant localized thermodynamic burden. Inspection of the mutant model in ChimeraX showed perturbation of the side-chain orientation and attenuation of the local hydrogen-bonding network at the FAD-binding site, together with increased steric packing around residue 72. Taken together, the clinical, genetic, and structural evidence support reclassification of p. Ser72Phe as likely pathogenic according to ACMG criteria, specifically applying the ClinGen ACADVL VCEP specifications. Conclusions: This study expands the ACADVL mutational spectrum and underscores the value of integrating sequencing, segregation, and structural bioinformatics in interpreting rare variants detected through NBS. Full article

Background: The monkeypox virus (MPXV) has attracted considerable global attention due to its potential to cause widespread outbreaks, necessitating the development of rapid and accurate diagnostic methods of significant clinical importance. A29, a key envelope protein of MPXV, represents a promising diagnostic target. Methods: A novel monoclonal antibody, D10, was isolated from the human Tomlinson I+J phage display library by biopanning against the recombinant A29 protein. The D10 Fab fragment was expressed and purified, and its binding affinity was characterized by biolayer interferometry. Molecular docking was performed to predict potential interacting residues. Specificity and detection performance were evaluated by direct and competitive enzyme-linked immunosorbent assay (ELISA). Results: D10 possesses a unique complementarity-determining region sequence and exhibits strong binding affinity toward the A29 protein. Structural modeling analysis suggested potential interacting residues of A29, including Gln67, Arg74, Asn75, Arg81, and Asn84, which may primarily interact with Ser10, Thr5, Gly49, Gly47, and Glu97 in the heavy chain of D10. The binding affinity, determined by biolayer interferometry, showed a dissociation equilibrium constant of 6.44 nM, indicating strong binding capability. Furthermore, competitive ELISA demonstrated that D10 binds selectively to the A29 protein, with a half-maximal inhibitory concentration of 1.88 μg/mL and a limit of detection of 0.12 μg/mL. Conclusions: Overall, this monoclonal antibody provides a valuable tool for the immunological detection of MPXV and holds potential for future clinical diagnostic applications. Full article

Background: Currently, increasing attention is being paid to the role of genes other than CFTR and their variants as factors modifying the course of cystic fibrosis (CF). One such gene is SLC26A9, which encodes a protein involved in chloride and bicarbonate transport across the epithelial cell membrane. Variants of SLC26A9, such as c.229G>A (p.Gly77Ser) and c.1885C>T (p.Pro629Ser), have been described in patients with severe and rapidly progressive CF. The aim of this study was to identify SLC26A9 variants in a group of 20 patients with CF. Methods: DNA was isolated from blood samples and collected from all patients. Fragments of exons 3 and 17 of the SLC26A9 gene were amplified by PCR and sequenced using the Sanger method. Results: An SLC26A9 variant was identified in two siblings. These patients were diagnosed with CF in adulthood and presented with moderate pulmonary symptoms without exocrine pancreatic insufficiency. In both siblings carrying the CFTR variants p.Phe508del and c.3140-26A>G, the SLC26A9 variant c.1847C>T (p.Pro616Leu) was detected. This variant has not been widely described in the literature and has not previously been associated with CF. Conclusions: The c.1847C>T (p.Pro616Leu) variant is located near a domain that may affect the transport function of the SLC26A9 protein. However, patients in whom the variant was identified did not present a severe disease phenotype. Further studies on larger patient cohorts are required, and at present this variant should be considered of uncertain significance in CF. Full article

Gold and silver nanoparticles have attracted extensive attention in SERS detection due to their excellent plasmonic properties. In this study, a high-performance SERS substrate was successfully prepared by a liquid–liquid self-assembly strategy. Driven by the Marangoni effect, Au-Ag nanoparticles spontaneously form a uniform and dense monolayer structure on the silicon wafer, constructing an efficient plasmon “hotspot” region, which significantly improves the detection sensitivity of the substrate. The performance of the SERS substrate was systematically evaluated using CV and Me B as Raman probe molecules. The results show that the substrate exhibits an excellent enhancement effect and good SERS sensitivity for both probe molecules. The characteristic vibration peak can be clearly identified, and the detection limit (LOD) of crystal violet is 6.76 × 10⁻¹¹ M. The substrate was applied to detect thiram residues in lake water with a LOD of 1.084 × 10⁻⁷ M, achieving highly sensitive detection. This study shows that Au-Ag nanoparticles deposited on silicon wafers by liquid–liquid self-assembly strategy can be used as a high-performance SERS substrate. It can be used for rapid and sensitive detection of thiram pesticide residues in water, and provides an efficient and feasible analysis tool for water environment safety monitoring. Full article

Bacterial infections pose a persistent global threat to public health, driving the demand for rapid, sensitive, and specific detection technologies applicable to disease diagnosis, food safety, and environmental monitoring. Conventional methods like plate culture and polymerase chain reaction are often hampered by lengthy procedures, dependence on complex instrumentation, and requirements for specialized personnel. The emergence of nanozymes and nanomaterials with enzyme-like catalytic activities has introduced a paradigm shift in biosensing, offering superior stability, cost-effectiveness, and tunable functionality compared to their natural counterparts. This review provides a comprehensive and systematic analysis of the latest advancements in nanozyme-mediated bacterial detection. It is structured around the primary signal transduction modalities: colorimetric, fluorescence, electrochemical, and surface-enhanced Raman scattering (SERS) analyses. For each approach, we outline the fundamental design principles, which commonly integrate a synergistic cascade of specific recognition, catalytic signal amplification, and signal readout, and present representative applications for detecting key pathogens like Staphylococcus aureus, Salmonella, and Listeria monocytogenes in complex samples. We evaluate and contrast the advantages, analytical performance, and appropriateness of these different platforms for various practical scenarios. Finally, we address current challenges, including achieving high specificity in complex matrices, precise modulation of nanozyme activity, and method standardization. Perspectives on future research directions aimed at developing next-generation, high-performance, and potentially portable bacterial detection systems are also provided. Full article